MPEMathematical Problems in Engineering1563-51471024-123XHindawi Publishing Corporation52925110.1155/2012/529251529251Research ArticleAdaptive Control and Synchronization of the Shallow Water ModelSangapateP.CattaniCarloDepartment of MathematicsFaculty of ScienceMaejo UniversityChiang Mai 50290Thailandmju.ac.th2012192201220120607201124092011260920112012Copyright © 2012 P. Sangapate.This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

The shallow water model is one of the important models in dynamical systems. This paper investigates the adaptive chaos control and synchronization of the shallow water model. First, adaptive control laws are designed to stabilize the shallow water model. Then adaptive control laws are derived to chaos synchronization of the shallow water model. The sufficient conditions for the adaptive control and synchronization have been analyzed theoretically, and the results are proved using a Barbalat's Lemma.

1. Introduction

A dynamical system is a system that changes over time. Chaotic systems are dynamical systems that are highly sensitive to initial conditions. Chaos phenomena in weather models were first observed by Lorenz equation; a large number of chaos phenomena and chaos behavior have been discovered in physical, social, economical, biological, and electrical systems.

Atmosphere is a dynamical system. An atmospheric model is a set of equations that describes behavior of the atmosphere. The shallow water model is simple model for the atmosphere. Shallow water model is the set of the equations of motion that describes the evolution of a horizontal structure, hydrostatic homogeneous, and incompressible flow on the sphere .

The control of chaotic systems is to design state feedback control laws that stabilize the chaotic systems. Control theory is an interdisciplinary branch of engineering and mathematics that deals with the behavior of dynamical systems. The usual objective of control theory is to calculate solutions for the proper corrective action from the controller that result in system stability.

Synchronization of chaotic systems is phenomena that may occur when two or more chaotic oscillators are coupled or when a chaotic oscillator drives another chaotic oscillator, because of the butterfly effect, which causes the exponential divergence of the trajectories of two identical chaotic systems started with nearby the same initial conditions. Synchronizing two chaotic systems is seemingly a very challenging problem in chaos literature .

In 1990, Pecora and Caroll  introduced a method to synchronize two identical chaotic systems and showed that it was possible for some chaotic systems to be completely synchronized. From then on, chaos synchronization has been widely explored in variety of fields including physical system , chemical systems , ecological systems , secure communications [11, 12], and so forth.

In most of the chaos synchronization approaches, the drive-response formalism has been used. If a particular chaotic system is called the drive system and another chaotic system is called the response system, then the idea of synchronization is to use the output of the drive system to control the response system so that the output of the response system tracks the output of drive system asymptotically stable.

This paper is organized as follows. Section 2 gives notations and definitions of the stability in the chaotic system. Section 3 presents the adaptive control chaos of the shallow water model. Section 4 presents adaptive synchronization of the shallow water model. The conclusion discussion is in Section 5.

2. Notations and Definitions

X denotes an infinite dimensional Banach Space with the corresponding norm , R denotes the real line.

Consider a nonlinear nonautonomous differential equation of the general form ẋ(t)=f(t,x(t)),tt0R,x(t0)=x0, where the state x(t) take values in X, f(t,x):R×XX is a given nonlinear function and f(t,0)=0, for all tR. The stability conditions were proposed and presented in .

Definition 2.1.

The zero solution of (2.1) is said to be stable if for every ε>0,t0R, there exists a number δ>0 (depending upon  ε and  t0) such that for any solution x(t) of (2.1) with x0<δ implies x(t)<ε, for all  tt0.

Definition 2.2.

The zero solution of (2.1) is said to be asymptotically stable if it is stable and there is a number δ>0 such that any solution x(t) with x0<δ satisfies limtx(t)=0.

Consider the control system ẋ(t)=f(t,x(t),u(t)),t0, where u(t) is the external control input. The adaptive control is the control method to design state feedback control laws that stabilize the chaotic systems.

Definition 2.3.

The control system (2.2) is stabilizable if there exists feedback control u(t)=k(x(t)) such that the system ẋ(t)=f(t,x(t),k(x(t))),t0, is asymptotically stable.

Consider two nonlinear systems ẋ=f(t,x(t)),ẏ=g(t,y(t))+u(t,x(t),y(t)), where x,yR,  f,gCr[R×R,  R],  uCr[R×R×R,  R],  r1,  R is the set  of nonnegative real number. Assume that (2.4) is the drive system, (2.5) is the response system, and u(t,x(t),y(t))  is the control vector.

Definition 2.4.

Response system and drive system are said to be synchronic if for any initial conditions  x(t0),  y(t0)  R,  limtx(t)-y(t)=0.

Lemma 2.5 (Barbalat’s lemma as used in stability).

For nonautonomous system, ẋ(t)=f(t,x(t)) If there exists a scalar function V(x,t) such that

V has a lower bound,

V̇0,

V̇(x,t) is uniformly continuous in time,

then limtV̇(x,t)=0 by applying the Barbalat’s Lemma to stabilize the chaotic systems.

3. Adaptive Control Chaos of the Shallow Water Model

A chaotic system has complex dynamical behaviors; those posses some special features, such as being extremely sensitive to tiny variations of initial conditions. In this section, adaptive control method is applied to control chaos shallow water model.

Shallow water model is the set of the equations of motion that describes the evolution of a horizontal structure, hydrostatic homogeneous, and incompressible flow on the sphere. Euler’s equations of motion of an ideal fluid are as follows: DuDt=-1ρpx+fv,DvDt=-1ρpy-fu,DwDt=-1ρpz-g, where ρ is the density of the fluid, p is the pressure, g is the gravity, and f is coliolis parameter. Using the hydrostatic approximation, pz=-ρg. This implies  Dw/Dt=0. Assume the pressure p of fluid is constant, this implies that p/t=0 and consider the continuity equation (or the incompressibility condition), ux+vy+wz=0. By solving for w/z and integrating with respect to z, then w can be expressed as wz=-(ux+vy),w=0h-(ux+vy)dz=-h(ux+vy). The surface (of the fluid) boundary condition on w is that the fluid particles follow the surface  (i.e.,  Dh/Dt=w|surface). Thus DhDt=-h(ux+vy). To get an expression for the pressure in the fluid, integrate the hydrostatic equation (3.2) from p=0 at the top downward, p(x,y,z)=hz-gρdz=(h-z)ρg. Take the partial derivatives of p (at the surface) with respect to x and y, px=x((h-z)ρg)=ρghx-1ρpx=-ghx,py=y((h-z)ρg)=ρghy-1ρpy=-ghy. Taking (3.2)–(3.7) into (3.1), so the shallow water model in Cartesian coordinates is as follows: DuDt=-ghx+fv,DvDt=-ghy-fu,DwDt=-h[ux+vy]. In the vector form, the shallow water model is as follows: V̇=-fk×V-Φ,Φ̇=-ΦV, where V=ui+vj is the horizontal velocity, Φ=gh is the geopotential height.

Consider the controlled system of (3.9) which has the formV̇=-fk×V-Φ+u1,Φ̇=-ΦV+u2, where u1,u2 is external control input which will drag the chaotic trajectory (V,Φ) of the shallow water model to equilibrium point E=(V̅,Φ̅) which is one of two steady states E0,E1.

In this case the control law is u1=-g(V-V̅),u2=-k(Φ-Φ̅), where k,g (estimate of k*, g*, resp.) are updated according to the following adaptive algorithm: ġ=μ(V-V̅)2,k̇=ρ(Φ-Φ̅)2, where μ,ρ is adaption gains. Then the controlled systems have the following form: V̇=-fk×V-Φ-g(V-V̅),Φ̇=-ΦV-k(Φ-Φ̅).

Theorem 3.1.

For g<g*,  k<k*, the equilibrium point E=(V̅,  Φ̅) of the system (3.13), (3.14) is asymptotically stable.

Proof.

Let us consider the Lyapunov function V(ξ1,ξ2,ξ3)=12[(V-V̅)2+(Φ-Φ̅)2+1μ(g-g*)2+1ρ(k-k*)2]. The time derivative of V is V̇=(V-V̅)V̇+(Φ-Φ̅)Φ̇+1μ(g-g*)ġ+1ρ(k-k*)k̇. By substituting (3.13)-(3.14) in (3.16), V̇=(V-V̅)[-fk×V-Φ-g(V-V̅)]+(Φ-Φ̅)[-ΦV-k(Φ-Φ̅)]+1μ(g-g*)μ(V-V̅)2+1ρ(k-k*)ρ(Φ-Φ̅)2. Let η1=(V-V̅),  η2=(Φ-Φ̅). Since (V̅,  Φ̅) is an equilibrium point of the uncontrolled system (3.9), V̇ becomes V̇=  η1[-fk×V-Φ-g(V-V̅)]+η2[-ΦV-k(Φ-Φ̅)]+(g-g*)η12+(k-k*)η22=(-fk×V)η1-Φη1-gη12-ΦVη2-kη22+(g-g*)η12+(k-k*)η22. It is clear that if we choose g<g* and k<k*, then V̇ is negative semidefinite. Since V is positive definite and V̇ is negative semidefinite, η1,η2,g,kL. From V̇(t)0, we can easily show that the square of η1,η2 is integrable with respect to t, namely, η1,η2L2. From (3.13)-(3.14), for any initial conditions, we have η1̇,η2̇L. By the well-known Barbalat’s Lemma, we conclude that η1,η2(0,0) as  t. Therefore, the equilibrium point E=(V̅,Φ̅) of the system (3.13)-(3.14) is asymptotically stable.

4. Adaptive Synchronization of the Shallow Water Model

In this section, the adaptive synchronization is introduced to make two of the shallow water model. The sufficient condition for the synchronization has been analyzed theoretically, and the result is proved using a Barbalat’s Lemma. Assume that there are two shallow water models such that the drive system is to control the response system. The drive and response system are given as V̇=-f1k1×V1-Φ1,Φ̇=-Φ1V1,V̇=-f2k2×V2-Φ2-u1,Φ̇=-Φ2V2-u2 where u=[u1,u2]T is the controller. We choose u1=k1eV,u2=k2eΦ, where k1,k2  0 and eV,eΦ  are the error states which are defined as follows eV=V2-V1,eΦ=Φ2-Φ1.

Theorem 4.1.

Let k1,f1,k1,k2  0 be property chosen so that the following matrix inequalities holds: P=(k1f1+k100k2)>0, then the two shallow water models (4.1) can be synchronized under the adaptive control (4.2).

Proof.

It is easy to see from (4.1) that the error system is eV̇=-f2k2×V2-Φ2+f1k1×V1+Φ1-u1,eΦ̇=-Φ2V2+Φ1V1-u2. Let ekf=k2f2-k1f1. Choose the Lyapunov function as follows: V(t)=12[eV2+eΦ2]. Then the differentiation of V along trajectories of (4.6) is V̇(t)=eVeV̇+eΦeΦ̇=eV[-f2k2×V2-Φ2+f1k1×V1+Φ1-u1]+eΦ[-Φ2V2+Φ1V1-u2]=-eV[f2k2×V2+Φ2-f1k1×V1-Φ1+u1]-eΦ[Φ2V2-Φ1V1+u2]=-eV[f2k2×V2-f1k1×V1+f1k1×V2-f1k1×V2]-eV[Φ2-Φ1]-eVu1-eΦ[Φ2V2-Φ1V1+Φ1V2-Φ1V2]-eΦu2=-eV[ekf×V2+f1k1(V2-V1)]-eV(Φ2-Φ1)-eVk1eV-eΦ[(Φ2-Φ1)V2+Φ1(V2-V1)]-eΦk2eΦ=-eV[ekf×V2+f1k1eV]-eVeΦ-eV2k1-eΦ[eΦV2+Φ1eV]-eΦ2k2=-eVekf×V2+f1k1eV2-eVeΦ-eV2k1-eΦ2V2-eΦΦ1eV-eΦ2k2-f1k1eV2-eV2k1-eΦ2k2-(f1k1+k1)eV2-k2eΦ2=-eTPe, where P is as in (4.4). Since V(t) is positive definite and V̇(t) is negative semidefinite, it follows that eV,eΦ,k1,f1,k1,k2L. From  V̇(t)  -eTPe, we can easily show that the square of eV,eΦ is integrable with respect to t, namely, eV,eΦL2. From (4.5), for any initial conditions, we have ėV(t),ėΦ(t)L. By the well-known Barbalat’s Lemma, we conclude that (eV,eΦ)(0,0) as t. Therefore, in the closed-loop system, V2(t)V1(t),Φ2(t)Φ1(t)  as t. This implies that the two shallow water models have synchronized under the adaptive controls (4.2).

5. Conclusions

In this paper, we applied adaptive control theory for the chaos control and synchronization of the shallow water model. First, we designed adaptive control laws to stabilize the shallow water model based on the adaptive control theory and stability theory. Then, we derived adaptive synchronization to the shallow water model. The sufficient conditions for the adaptive control and synchronization of the shallow water model have been analyzed theoretically, and the results are proved using a Barbalat’s Lemma.

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